Prediction of traffic accidents trend with learning methods: a case study for Batman, Turkey

Abstract

Assessing the trend of fatalities in recent years and forecasting road accidents enables society to make appropriate planning for prevention and control. This study analyses the road traffic accident data between the years 2013 and 2022 obtained for the province of Batman in Turkey, where it has not been considered before. The scope of the data analysed includes the fatalities and injuries of drivers, passengers and pedestrians. The road accident forecast for the next ten years up to 2032 is the focus of this study and numerous analyses using learning methods such as State Space Models (SSM), Artificial Neural Networks (ANN), Autoregressive Integrated Moving Average (ARIMA) and hybrid models (CNN + LSTM and Attention + GRU) have been performed on the available data. The predictions made with the above models give results with acceptable accuracy. However, they give different results depending on the parameters used. The models created with the data studied show that the number of road accidents and the related deaths and injuries will continue to increase over the next 10 years, starting in 2022. If the causes of road accidents are not eliminated and the situation remains stable as it is in 2022, the number of accidents, deaths and injuries is expected to double by 2032.